China* 0.6% (IFR)
China† 0.66%
France† 0.7%
Brazil‡ 1%
Spain‡ 1%
*Estimate based on natural experiment. †Estimate based on modelling.
‡Estimate based on prevalence data.HOW DEADLY IS SARS COV 2?
The infection fatality rate (IFR) is the proportion
of people with COVID-19 who will die from the
disease. Estimates are for specific regions, and
can vary depending on demographics,
health-care access and study methodology.overestimated the deadliness of SARS-CoV-2,
the virus that causes COVID-19, and then later
analyses underestimated its lethality. Now,
numerous studies — using a range of methods
— estimate that in many countries, 5–10 people
will die for every 1,000 people with COVID-19.
“The studies I have any faith in are tending to
converge around 0.5–1%,” says Russell.
But some researchers say that convergence
between studies could just be coincidence.
For a true understanding of how deadly the
virus is, scientists need to know how readily
it kills different groups of people. The risk of
dying from COVID-19 can vary considerably,
depending on age, ethnicity, access to health
care, socio-economic status and underlying
health conditions. More high-quality surveys
of different groups are needed, these
researchers say.
IFR is also specific to a population and
changes over time as doctors get better
at treating the disease, which can further
complicate efforts to pin it down.
Getting the number right is important
because it helps governments and individuals
to determine appropriate responses.
“Calculate too low an IFR, and a community
could under-react, and be underprepared.
Too high, and the overreaction could be at
best expensive, and at worst [could] also add
harms from the overuse of interventions like
lockdowns,” says Hilda Bastian, who studies
evidence-based medicine at Bond University
in the Gold Coast, Australia.
Bridging the gap
Some of the first indications of the virus’s
deadliness were gleaned from the total
number of confirmed cases in China. In late
February, the World Health Organization
crudely estimated that 38 people had died
for every 1,000 with confirmed COVID-
diagnoses. The death rate among these
people — known as the case fatality rate —
reached as high as 58 out of 1,000 in Wuhan,
the city where the virus emerged. But
such estimates exaggerated the disease’s
deadliness because they did not account for
the many people who had the virus but were
not tested, obscuring the outbreak’s true
spread.
Researchers tried to address this gap
by estimating the IFR from models that
projected the virus’s spread. The result from
these early analyses hovered around 0.9% —
9 deaths for every 1,000 people infected —
with a broader range of 0.4–3.6%, says Verity.
His own modelling estimated an overall IFR
for China of 7 deaths for every 1,000 people
infected, increasing to 33 per 1,000 among
those aged 60 or older^1 (see ‘How deadly is
SARS-CoV-2?’).
Russell’s team also used data gathered from
a large COVID-19 outbreak on the Diamond
Princess cruise ship in early February to
estimate an IFR in China. Almost all of the
3,711 passengers and crew members were
tested, enabling researchers to count the total
number of infections, including asymptomatic
ones, and deaths in a known population. From
this, the team estimated an IFR of 0.6%, or
6 deaths for every 1,000 infected people^2.
“The intention of these studies was to
gain some ball-park estimates of how deadly
COVID-19 is,” says Verity.
But researchers also had to make
complicated estimates, which still need to
be verified, about the number of confirmed
cases and the actual number of infected
people. “There is value to obtaining rapid
early estimates of the IFR, [but] these should
be updated as a matter of urgency once better
data becomes available,” says Verity.
Widespread population surveys that test
people for antibodies to the virus, known as
seroprevalence surveys, were expected tohelp refine IFR estimates even further. About
120 such surveys are under way worldwide.
But results from the first antibody studies
only muddied the water, suggesting that the
virus was less deadly than previously thought.
“It got a bit messy,” says Russell.
One of the earliest studies tested 919 people
in the German town of Gangelt, where a large
outbreak had occurred^3. Of these people,
about 15.5% had antibodies against the virus
— 5 times higher than the percentage of people
known to have had COVID-19 in the town at
the time. The figure was used to estimate an
IFR of 0.28%. But researchers noted that the
study was based on a relatively small number
of people.
Other early seroprevalence studies did not
properly account for the lack of sensitivity
and specificity in the antibody test kits that
were used, or for discrepancies between the
sampled and underlying populations, says
Verity.These issues could have inflated estimates
of the total number of infected people and
so made the virus seem less deadly, he says.
Equally, if COVID-19 deaths go undetected
— a problem in many countries that aren’t
testing all deceased people for the virus —
that, too, can bias the fatality rate, says Gideon
Meyerowitz-Katz, an epidemiologist at the
University of Wollongong, Australia.
Some larger seroprevalence studies have
emerged in recent weeks, and these estimate
a higher fatality rate than do early studies.
One survey^4 , posted on medRxiv, of more
than 25,000 people across Brazil, estimated
an IFR of 1%.
Another survey that tested more than
60,000 people across Spain reports a
prevalence of 5%, although the results have
not been formally analysed (see go.nature.
com/2brqo2c). The survey team did not
calculate a fatality rate itself but, on the basis
of the results, Verity estimates that Spain has
an IFR of around 1% — or 10 deaths for every
1,000 infected individuals.
Several researchers, including Russell
and Verity, find it interesting that a growing
number of studies from different regions
have estimated IFRs in the range of 0.5–1%.
But other scientists are cautious about
suggestions of agreement. “The trend is
potentially more luck than anything else,”
says Meyerowitz-Katz.
Marm Kilpatrick, an infectious-disease
researcher at the University of California,
Santa Cruz, also notes that most of the
serological data haven’t been published in
scientific manuscripts. It’s hard to know when
and how they were collected, and to properly
calculate an IFR that accounts for the delay
between people getting infected and dying,
he says.
Kilpatrick and others are eagerly awaiting
large studies that estimate fatality rates across
age groups and among those with pre-existing
health conditions, which will provide the most
accurate picture of how deadly the disease
is. One of the first studies to account for the
effect of age was posted on a preprint server on
12 June^5. The study, based on seroprevalence
data from Geneva, Switzerland, estimates an
IFR of 0.6% for the total population, and 5.6%
for people aged 65 and older.
The results have not been peer reviewed,
but Kilpatrick says the study addresses many
of the issues in previous seroprevalence
surveys. “This study is fantastic. It’s precisely
what should be done with all of the serological
data,” he says.- Verity, R. et al. Lancet 20 , 669–677 (2020).
- Russell, T. W. et al. Euro Surveill. 25 , 2000256 (2020).
- Streeck, H. et al. Preprint at medRxiv https://doi.
org/10.1101/2020.05.04.20090076 (2020). - Hallal, P. C. et al. Preprint at medRxiv https://doi.
org/10.1101/2020.05.30.20117531 (2020). - Perez-Saez, F. et al. Preprint at OSF https://doi.
org/10.31219/osf.io/wdbpe (2020). SOURCES: CHINA*: REF. 2; CHINA†: REF. 1; FRANCE: H. SALJE
ET AL. SCIENCE
HTTP://DOI.ORG/DVT3 (2020);BRAZIL: REF. 4; SPAIN: SPANISH MINISTRY OF HEALTH, CONSUMER AFFAIRS AND SOCIAL WELFARE 2020 REPORT“There is value to
obtaining rapid early
estimates of the
infection fatality rate.”468 | Nature | Vol 582 | 25 June 2020
News in focus
©
2020
Springer
Nature
Limited.
All
rights
reserved.